Senior Data Engineer
Builds and maintains scalable data infrastructure for broadband infrastructure monitoring and BEAD program, focusing on Airflow DAGs, AWS/GCP pipelines, and AI/ML-integrated data workflows.
- Compensation
- $190K–$240K / yr
- Location
- Remote
- Work setup
- Remote
- Seniority
- Senior
- Employment
- Not specified
- Experience
- 5+ years
- Category
- Data engineering
- Posted
Skills
Must have
- 5+ years of data engineering experience, owning and operationally supporting production systems end-to-end
- Strong proficiency in SQL (PostgreSQL, Athena/Presto) and Python; production-quality code
- Deep hands-on experience with AWS data services (S3, Athena, RDS, ECS, Lambda, IAM, CloudWatch)
- Experience with Apache Airflow or similar orchestration tools in production
- Experience with large-scale time-series, event-based, or streaming data systems
- Experience with version control (GitHub/GitLab)
- Familiarity with vector databases and LLM integration (LangChain, AWS Bedrock)
- Experience building data quality frameworks or automated validation systems
- Demonstrated ability to mentor engineers and contribute to team culture
Nice to have
- Experience managing data for SaaS platforms
- Experience with large-scale spatiotemporal data pipelines (PostGIS, spatial indexing, tiling)
Full description
Ready is expanding its data engineering team to support rapid growth across our broadband infrastructure monitoring platform and the BEAD (Broadband Equity, Access, and Deployment) program. We're looking for a Senior Data Engineer who thrives in complex pipeline environments, is comfortable across AWS and GCP, and can bring structure and reliability to large-scale government data programs.
You'll work closely with the Head of Data and a cross-functional team to build, optimize, and maintain the data infrastructure that powers multi-state broadband programs. This is a hands-on engineering role — expect to spend most of your time building Airflow DAGs, designing data models and making data move reliably at scale.
About your role at Ready ⚡️
- Partner with product and engineering teams to understand requirements and translate them into the right technical architecture — balancing cost, performance, and long-term maintainability
- Evaluate and recommend data tools, frameworks, and infrastructure choices with a pragmatic, product-first mindset
- Contribute to roadmap discussions and help the team make informed build-vs-buy decisions
- Design, implement, and maintain scalable data infrastructure on AWS (S3, Athena, RDS/PostgreSQL, ECS, Lambda, Secrets Manager, CloudWatch)
- Own data lake and warehouse architecture — partitioning strategies, storage optimization, and data lifecycle management
- Build and maintain production-grade Apache Airflow DAGs for ingestion, transformation, and export workflows
- Ensure observability across the data platform — monitoring, alerting, and fast incident resolution
- Build and maintain robust DBT pipelines with data quality checks and well-structured data modeling
- Design and maintain robust database schemas to support multi-state, multi-tenant program data
- Write and optimize SQL queries across PostgreSQL, Redshift and Athena for analytical and operational workloads
- Develop reusable data models, utilities, and shared Python packages that raise the bar for the whole team
- Design data models and infrastructure for large-scale time-series and event-based data management at scale
- Implement efficient ingestion, storage, and retrieval patterns for high-frequency temporal datasets
- Work with vector databases to support AI-powered features and semantic search capabilities
- Integrate LLM workflows into ELT pipelines using AWS Bedrock, LangChain, and related frameworks
- Build AI-assisted data comparison, validation, and enrichment pipelines that run reliably in production